---
title: "AMPs ARGENTINA"
output: 
  flexdashboard::flex_dashboard:
    theme: lumen
    source: embed
    logo: logo.png
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(plotly)
library(spocc)
library(readr)
library(reshape2)
library(leaflet)
library(xts)
library(dygraphs)
library(plotly)
library(lubridate)
library(dplyr)
library(htmltools)
library(DT)
library(shiny)
library(xts)
library(htmlwidgets)
library(vegan)

library(RColorBrewer)
palette(brewer.pal(8, "Set2"))


```

```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#READ both files metadata and percent cover 

# Suppress summaries info
options(dplyr.summarise.inform = FALSE)

#DATA
##Data folder
Data <- "DATA"

#We download from CORALNET two files, 1-species cover information and 2- metadata of photoquadrats:
# 1- metadata.csv
# 2- percent_cover.csv

library(readr)
cover <- read_csv(file.path(Data, "percent_covers.csv"))#read cover data
metadata <- read_csv(file.path(Data,"metadata.csv"))#read metadata
AMP_list <- read_csv(file.path(Data,"AMP_list.csv"))

#Merge photoquadrat.metadata and photoquadrat.cover
AMP<- merge(metadata,cover, by.x = "Name", by.y ="Image name", all.x = TRUE) 


#Remove original data frames from enviroment
rm(cover)
#rm(metadata)

# Reemplazar "CABO DOS BAHIAS" por "PIMCPA_NORTE" en la columna "locality" de AMP
AMP$locality <- ifelse(AMP$locality == "CABO DOS BAHIAS", "PIMCPA_NORTE", AMP$locality)
# Reemplazar "PIMCPA" por "PIMCPA_SUR" en la columna "locality" de AMP
AMP$locality <- ifelse(AMP$locality == "PIMCPA", "PIMCPA_SUR", AMP$locality)




#all seaweed
AMP_original <- AMP

AMP$algae <- as.numeric(paste(AMP$MAA +AMP$MAEC + AMP$MAEF+ AMP$MAEN+ AMP$MAF+ AMP$MAG+ AMP$MALA+ AMP$MALCB+ AMP$MAS))


#Create long type dataframe 
library(reshape)
AMP_long = melt(AMP, id.vars = 1:22, measure.vars = 23:ncol(AMP), variable_name = "CATAMI", value_name ="cover", na.rm = T)
#rename columns because the ontop command is not working 
colnames(AMP_long)[23] <- "CATAMI"
colnames(AMP_long)[24] <- "cover"

#Calculate mean, SD, SE for cover data by factors 
library(doBy)
Coverdata <- summaryBy(cover ~ CATAMI + strata,data=AMP_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})

library(doBy)
Coverdata_AMPs <- summaryBy(cover ~ CATAMI + strata +locality ,data=AMP_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})

library(dplyr)
library(lubridate)
AMP <- AMP %>%
  mutate(year = lubridate::year(Date))  %>%
  mutate(month = lubridate::month(Date))  %>%
  mutate(estacion = ifelse(month %in% c(12, 1, 2, 3, 4, 5), "Cálida", "Fría"))%>%
  select(Name,Date, year,month,estacion,everything())

photo_bydate = as.data.frame(table(AMP$year,AMP$site,AMP$locality,AMP$strata))
colnames(photo_bydate)=c("Fecha","Sitio","Localidad","Estrato","n fotocuadrantes")  


#SST
#getSST.r was used to get data 
## get sampling event dates
samplingDates = unique(AMP$Date)

## read SST values
library(readr)
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
SST = read_csv("ISLAPINGUINO_SST.csv")
SST.clim = read_csv("ISLAPINGUINO_Climatology.csv")
setwd("..")# original WD


library(plotly)
library(dplyr)
library(cols4all)
# Obtener las categorías únicas en la columna 'CATAMI'
categorias_unicas <- unique(Coverdata_AMPs$CATAMI)
num_categorias <- length(categorias_unicas)

# Obtener una paleta de colores de cols4all con 18 colores
paleta_cols4all <- c4a("poly.alphabet", 18)

# Crear un vector de colores asignados a las categorías
colores_categoria <- paleta_cols4all[match(Coverdata_AMPs$CATAMI, categorias_unicas)]

# Asignar los colores al dataframe en una nueva columna llamada 'Color'
Coverdata_AMPs$Color <- colores_categoria

# Obtener las localidades únicas en la columna 'locality'
localidades_unicas <- unique(Coverdata_AMPs$locality)

Coverdata_AMPs$CATAMI


# Definir el dataframe con los nombres actualizados
nombres_actualizados <- recode(Coverdata_AMPs$CATAMI,
                                "CNCA" = "Anémonas coloniales",
                                "CNTR" = "Anémonas",
                                "CRB" = "Cirripedios",
                                "MOB" = "Bivalvos",
                                "MOG" = "Gasterópodos",
                                "SP" = "Esponja",
                                "WPOT" = "Tubo poliqueto",
                                "SC" = "Suelo Desnudo",
                                "Unc" = "Sin definir",
                                "MAA" = "Algas calcáreas",
                                "MAEC" = "Algas ramas gruesas",
                                "MAEF" = "Algas ramas finas",
                                "MAEN" = "Algas incrustantes",
                                "MAF" = "Algas filamentosas",
                                "MAG" = "Algas globosas",
                                "MALA" = "Algas laminares",
                                "MALCB" = "Algas formadoras de bosque",
                                "MAS" = "Algas hojas laminares finas")

# Reemplazar la columna original con los nombres actualizados
Coverdata_AMPs$CATAMI <- nombres_actualizados


```


# Mapa

Column {data-width=600}
-------------------------------------

### Sitios de muestreo
```{r map, message=FALSE, warning=FALSE}

# Combinar las columnas year y estacion en una columna llamada tiempo
AMP$tiempo <- paste(AMP$year, AMP$estacion, sep = "_")

# Crear el mapa
map <- leaflet(options = leafletOptions(zoomControl = TRUE,
                                        zoomSnap = 0.25,
                                        zoomDelta = 1)) %>%
  addProviderTiles('Esri.WorldImagery',
                   options = providerTileOptions(maxNativeZoom=19,maxZoom=100),
                   group = "Satelite"
  ) %>%
  addProviderTiles(
    "OpenStreetMap",
    group = "Mapa para zoom"
  ) %>%
  addLayersControl(
    baseGroups = c("Satelite", "Mapa para zoom"),
    position = "topleft"
  )

# Añadir puntos para cada tiempo como capas separadas
for (tiempo in unique(AMP$tiempo)) {
  map <- map %>%
    addCircleMarkers(
      data = AMP[AMP$tiempo == tiempo, ], 
      ~Longitude, 
      ~Latitude,
      weight = 0.5,
      radius = 4,
      fillOpacity = 0.5,
      stroke = TRUE,
      popup = ~paste("Strata:", strata, "<br>", "Name:", Name),
      color = ~factor(strata),
      fillColor = ~factor(strata) %>% 
        {ifelse(. == "HIGHTIDE", "red", ifelse(. == "MIDTIDE", "blue", "green"))},
      group = as.character(tiempo)  # Asignar cada punto a su tiempo correspondiente como un grupo separado
    )
}

# Agregar control de capas para seleccionar el tiempo
map <- map %>%
  addLayersControl(
    overlayGroups = unique(AMP$tiempo),  # Lista de tiempos para seleccionar
    options = layersControlOptions(collapsed = FALSE)  # Panel desplegado por defecto
  ) %>%
  addControl(html = "<div id='legend'>Estratos: <br><i style='background: red'></i> Alta <br><i style='background: blue'></i> Media <br><i style='background: green'></i> Baja </div>",
             position = "bottomright")

map  # Mostrar el mapa



```

Column {data-width=400}
-------------------------------------

### Fotoquadrantes
```{r}
library(dplyr)
library(tidyr)

# Calcular la tabla con todos los valores
table_data <- as.data.frame(table(AMP$year, AMP$month, AMP$locality))

# Filtrar la tabla para eliminar filas con n igual a cero
table_filtered <- table_data[table_data$Freq != 0, ]

# Renombrar las columnas
colnames(table_filtered) <- c("Año", "Mes", "Localidad", "n")

# Agregar una columna que clasifique los meses como "Cálida" o "Fría"
table_filtered <- table_filtered %>%
  mutate(Estacion = ifelse(Mes %in% c(12, 1, 2, 3, 4, 5), "Cálida", "Fría"))

# Calcular la suma total de la columna n agrupada por Año, Estacion y Localidad
total_n <- table_filtered %>%
  group_by(Año, Estacion, Localidad) %>%
  summarize(Total_n = sum(n)) %>%
  ungroup() %>%
  mutate(Total_n = ifelse(is.na(Total_n), 0, Total_n))  # Reemplazar NA con 0 en caso de que no haya datos para una combinación

# Calcular la suma total de la columna n agrupada por Año y Estacion
total_estacion <- total_n %>%
  group_by(Año, Estacion) %>%
  summarize(Total_n = sum(Total_n)) %>%
  ungroup() %>%
  mutate(Localidad = "Total")  # Agregar "Total" como localidad para estas filas

# Agregar una fila al final con la suma total de la columna n por Año
total_por_año <- total_n %>%
  group_by(Año) %>%
  summarize(Total_n = sum(Total_n)) %>%
  mutate(Estacion = "Total", Localidad = "Total")

# Combinar la tabla total_n con la fila de suma total de estación
tabla_final <- bind_rows(total_n, total_estacion, total_por_año)

# Mostrar la tabla con el total de la columna n agrupada por Año, Estacion y Localidad
knitr::kable(tabla_final, row.names = FALSE)



```


```{r taxacover, eval=FALSE, include=FALSE}
# Cobertura de organismos vivos
# Use this when using percent_covers.csv and full names from CoralNet
# taxacover = AMP_long %>% filter(CATAMI != "Substrate..Consolidated..hard.") %>% 
#   group_by(site, strata, Image.ID) %>% 
#   summarise(sumcover = sum(cover, na.rm=T))

# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
taxacover = AMP_long %>% filter(CATAMI != "SC", CATAMI !="algae") %>% 
  group_by(locality, strata, Name) %>% 
  summarise(sumcover = sum(cover, na.rm=T))

pp = ggplot(taxacover, aes(x=factor(strata,level=c('LOWTIDE', 'MIDTIDE', 'HIGHTIDE')), sumcover, fill=strata))
pp = pp + geom_boxplot() + ylab("% Cobertura de todas las especies por cuadrante") + xlab("")+
  facet_grid(~locality) + 
  theme_bw(base_size = 10) + theme(legend.position = "none",axis.text.x = element_text(angle = 90, hjust = 1),strip.text = element_text(size = 5))
ggplotly(pp)
```


```{r eval=FALSE, include=FALSE}
### Coberturas de Molluscos
#<!-- <img src="images/AMP20053.JPG" width="400px"/> -->
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)

# Use this when using percent_covers.csv and full names from CoralNet
# value <- Coverdata[Coverdata$CATAMI == "Molluscs..Bivalves", ]

# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "MOB", ]

Molluscs <- e_charts() %>% 
  e_gauge(round(value$cover.mean[1], 0), 
          "ALTO", 
          center = c("20%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(1, "green"),
                c(1, "green"),
                c(1, "green")
              )
            ))) %>% 
    e_gauge(round(value$cover.mean[3], 0), 
          "MEDIO", 
          center = c("50%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(0.20, "red"),
                c(0.4, "yellow"),
                c(1, "green")
              )
            ))) %>% 
      e_gauge(round(value$cover.mean[2], 0), 
          "BAJO", 
          center = c("80%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(1, "green"),
                c(1, "green"),
                c(1, "green")
              )
            ))) %>% 
  e_title("Moluscos (%)")

Molluscs

# # High tide
# gauge(value$cover.mean[1], min = 0, max = 100, symbol = '%', 
#       gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
#       label = "Estrato alto"
#       )
# 
# # Mid tide
# gauge(value$cover.mean[3], min = 0, max = 100, symbol = '%', 
#       gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
#       label = "Estrato medio"
#       )
# 
# # Low tide
# gauge(value$cover.mean[2], min = 0, max = 100, symbol = '%', 
#       gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
#       label = "Estrato bajo"
#       )
```


```{r eval=FALSE, include=FALSE}
### Coberturas de Macro-Algas
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)

# Use  short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "algae", ]


MAS <- e_charts() %>% 
  e_gauge(round(value$cover.mean[1], 0), 
          "ALTO", 
          center = c("20%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(1, "green"),
                c(1, "green"),
                c(1, "green")
              )
            ))) %>% 
    e_gauge(round(value$cover.mean[3], 0), 
          "MEDIO", 
          center = c("50%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(1, "green"),
                c(1, "green"),
                c(1, "green")
              )
            ))) %>% 
      e_gauge(round(value$cover.mean[2], 0), 
          "BAJO", 
          center = c("80%", "20%"), 
          radius = "35%",
          color = "black",
          min=0, 
          max=100,
          splitNumber = 5,
          axisLine = list(
            lineStyle = list(
              color=list(
                c(0.10, "red"),
                c(.20, "yellow"),
                c(1, "green")
              )
            ))) %>% 
  e_title("Macro-algas (%)")

MAS
```

# Riqueza de categorias CATAMI
```{r}
#Factors
locality <- AMP_original$locality
site <- AMP_original$site
strata <- AMP_original$strata
state<- AMP_original$state
richness<-AMP_original[,-(1:22)]
sppnumber <- specnumber(richness)


#Data Frame with spp number per photoquadrat
spp <- data.frame(state,locality,site,strata,sppnumber)


# Definir el orden deseado de la columna locality
orden_sitios <- rev(c("MAR DEL PLATA", "ISLOTE LOBOS", "PUNTA BS AS",
                  "PUNTA LOMA", "PUNTA TOMBO", "PIMCPA_NORTE",
                  "PIMCPA_SUR", "PUNTA BUQUE", "MAKENKE",
                  "MONTE LEON"))

# Convertir la columna locality a un factor con el orden deseado
spp$locality <- factor(spp$locality, levels = orden_sitios)

# Ordenar el dataframe spp por la columna locality
spp <- spp[order(spp$locality), ]


# Crear un boxplot
spp$strata <- factor(spp$strata, levels = c("HIGHTIDE", "MIDTIDE", "LOWTIDE"))

# Graficar con facet_grid ordenado
catamirichness <- ggplot(spp, aes(x = locality, y = sppnumber)) +
  geom_boxplot() + theme_bw() +
  labs(x = "Sitio", y = "Cantidad de categorias CATAMI") + 
  theme(axis.text.x = element_text(size = 12)) + 
  coord_flip() + 
  facet_grid(~strata)
ggplotly(catamirichness)

```


```{r include=FALSE}

library(patchwork)
# Crear una lista para almacenar los gráficos generados en el bucle
plot_list <- list()

# Iniciar un loop para cada localidad
for (localidad in localidades_unicas) {
  # Filtrar los datos para la localidad actual
  Coverdata_AMPs_site <- filter(Coverdata_AMPs, locality == localidad)
  
  # Crear un gráfico de torta para cada estrato
  sel_data.L <- filter(Coverdata_AMPs_site, strata == "LOWTIDE", cover.mean > 0, CATAMI != "algae")
  sel_data.M <- filter(Coverdata_AMPs_site, strata == "MIDTIDE", cover.mean > 0, CATAMI != "algae")
  sel_data.H <- filter(Coverdata_AMPs_site, strata == "HIGHTIDE", cover.mean > 0, CATAMI != "algae")
  
  p <- plot_ly(labels = ~CATAMI, values = ~cover.mean, legendgroup = ~CATAMI, textinfo = 'label+percent',
               marker = list(colors = ~Color)) %>%  # Aquí asignamos los colores desde la columna 'Color'
    add_pie(data = sel_data.L, name = "Bajo", title = 'Estrato Bajo', domain = list(row = 0, column = 0)) %>%
    add_pie(data = sel_data.M, name = "Medio", title = 'Estrato Medio', domain = list(row = 0, column = 1)) %>%
    add_pie(data = sel_data.H, name = "Alto", title = 'Estrato Alto', domain = list(row = 0, column = 2)) %>%
    layout(title = localidad, showlegend = T,
           grid = list(rows = 1, columns = 3),
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
  
  # Agregar el gráfico a la lista
  plot_list[[length(plot_list) + 1]] <- p
}


```

# Cobertura por categorias

Column1 {.tabset .tabset-fade}
-------

### MAR DEL PLATA
```{r}
plot_list[[3]]
```

### ISLOTE LOBOS
```{r}
plot_list[[1]]
```

### PUNTA BS AS
```{r}
plot_list[[6]]
```

### PUNTA LOMA
```{r}
plot_list[[8]]
```

### PUNTA TOMBO
```{r}
plot_list[[9]]
```

### PIMCPA_NORTE
```{r}
plot_list[[4]]
```

### PIMCPA_SUR
```{r}
plot_list[[5]]
```

### PUNTA BUQUE
```{r}
plot_list[[7]]
```

### MAKENKE
```{r}
plot_list[[2]]
```

### MONTE LEON
```{r}
plot_list[[10]]
```